Teaching plan for the course unit

 

 

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General information

 

Course unit name: Artificial Intelligence in Biomedical Engineering

Course unit code: 366687

Academic year: 2021-2022

Coordinator: Roser Sala Llonch

Department: Department of Biomedical Sciences

Credits: 3

Single program: S

 

 

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

35

 

-  Lecture

Face-to-face

 

11

 

-  Laboratory session

Face-to-face

 

14

 

-  Seminar

Face-to-face

 

10

Supervised project

10

Independent learning

30

 

 

Competences to be gained during study

 

   -

To use IT tools to search for reference resources or information related to medical technologies and bioengineering (Personal).

   -

To be able to work independently (Personal).

   -

To gain knowledge of basic and technological subjects required to learn new methods and technologies and ensure versatility and the ability to adapt to new situations (Personal).

   -

To be able to take further studies and to develop a positive attitude in order to keep knowledge up-to-date in a process of lifelong learning. To have sufficient depth of knowledge to start postgraduate studies in the field of advanced biomedical engineering.

   -

As far as possible, the gender perspective will be incorporated throughout the development of the course.

 

 

Learning objectives

 

Referring to knowledge

To understand the basis of artificial intelligence in the context of biomedical engineering

 

To understand the nature of data in medicine and biomedicine.

 

To know the basic methods in machine learning and deep learning

 

Referring to abilities, skills

To be able to design and implement an AI-based application

 

To know the problems related to data inhomogeneity and inaccurate labelling, and to be able to design solutions to overcome these problems.

 

To be able to program the main machine learning and deep learning algorithms.

 

Referring to attitudes, values and norms

To be able to evaluate the ethical impact of artificial intelligence in biomedical engineering. 

 

 

Teaching blocks

 

1. Artificial Intelligence in Biomedical Engineering

1.1. Machine Learning and Deep Learning

1.2. Discriminability and interpretability. 

1.3. Characteristics of the biomedical data: feature extraction

1.4. Examples of applications

2. Main algorithms in Machine Learning

2.1. Linear SVM and other linear classifiers

2.2. Non-linear classifiers

2.3. Unsupervised algorithms

2.4. Training and validation

3. Deep Learning

3.1. Theoretical basis

3.2. Main algorithms

4. Applications in Biomedical Engineering

4.1. Databases and advanced preprocessing methods

4.2. Real-life examples

 

 

Teaching methods and general organization

 

The course will be organised into: class lectures, seminars, practical sessions, and project development sessions.

The lectures will provide the theoretical basis of the main algorithms and definitions. 

The seminars will consist on class activities to see different applications of AI in the biomedical and clinical setting. 

The practical sessions will consist on short guided exercises focused on single aspects of the course. These sessions will be designed as hands-on tutorials using different programming languages (R/python/matlab).   

In addition, students will develop a project consisting of an application of artificial intelligence based on real-life problems.

The course will be taught completely in English. 

 

 

Official assessment of learning outcomes

 

The course grade will be a weighted average of the different activities performed during the semester. It will be calculated as follows: 

  • Continuous assessment (40%): Practical sessions and other activities during the course. 
  • Project (40%):  presentation of written report and validation of the code. 
  • Final exam (20%): written assessment of theoretical and practical aspects of the course. 


Repeat assessment: Students who fail to pass the subject can repeat assessment. To be eligible to repeat assessment, students must abide by the Academic Committee’s regulations for the bachelor’s degree. Students who have sit the first assessment procedure and fail to pass can repeat assessment of the final examination. Practical sessions cannot be repeated.

Exam revision: The exam revision system follows the UB regulations for assessment and grading of learning outcomes.

 

Examination-based assessment

Students can request single assessment and waive their right to continuous assessment before the established deadline.

The course grade will be a weighted average of the different activities performed during the semester. It will be calculated as follows: 

  • Project (40%):  presentation of written report and validation of the code. 
  • Final exam (60%): written assessment of theoretical and practical aspects of the course. 


Repeat assessment: Students who fail to pass the subject can repeat assessment. To be eligible to repeat assessment, students must abide by the Academic Committee’s regulations for the bachelor’s degree. Students who have sit the first assessment procedure and fail to pass can repeat assessment of the final examination. Practical sessions cannot be repeated.

Exam revision: The exam revision system follows the UB regulations for assessment and grading of learning outcomes.